kamino final project

Deaths Caused By Illegal Drug use in East Africa.

knitr::include_graphics("assets/nt2.png", dpi=00)

masthead <- "assets/nt2.png"

Author: Nahom Tesfaye

## Loading Google fonts (https://fonts.google.com/)
font_add_google("Inconsolata", "inconsolata")
# turn on showtext
showtext_auto()

Task 1: INGEST

Number of deaths from illicit drugs, World wide from 1990- 2017

df<-read.csv('hw/drug overdoses.csv')#(World Data For Death Caused By Drug Over Dose)


df_1<-df
df_1$Deaths.Opioid<-format(round(df_1$Deaths.Opioid,0), nsmall=0)
df_1$Deaths.Cocaine<-format(round(df_1$Deaths.Cocaine,0), nsmall=0)
df_1$Deaths.other.drug<-format(round(df_1$Deaths.other.drug,0), nsmall=0)
df_1$Deaths.Amphetamine<-format(round(df_1$Deaths.Amphetamine,0), nsmall=0)
df_1

Task 2: WRANGLE

Total deaths from illicit drugs in East Africa from 1990-2017

df_2<-df
df_2<-df_2%>%select(Entity,Deaths.Opioid,Deaths.Cocaine,Deaths.other.drug,Deaths.Amphetamine)%>%rename(Country='Entity')
df_2<-df_2%>%arrange(Country)%>%filter((Country=="Mauritius") | (Country=="Uganda") | (Country=="Rwanda")| (Country=="Kenya")| (Country=="Ethiopia")| (Country=="Tanzania")| (Country=="Eritrea")| (Country=="Madagascar")| (Country=="Somalia")| (Country=="Djibouti"))
#%>%select(Country,`Deaths.Opioid`, `Deaths.Cocaine`,`Deaths.other.drug`,`Deaths.Amphetamine`)



df_11<-df_2
df_11$Deaths.Opioid<-format(round(df_11$Deaths.Opioid,0), nsmall=0)
df_11$Deaths.Cocaine<-format(round(df_11$Deaths.Cocaine,0), nsmall=0)
df_11$Deaths.other.drug<-format(round(df_11$Deaths.other.drug,0), nsmall=0)
df_11$Deaths.Amphetamine<-format(round(df_11$Deaths.Amphetamine,0), nsmall=0)
df_11

CHECKING MISSING VALUES

missing_stats <- purrr::map_df(df_2, ~ sum(is.na(.))) %>%
  gather('Column name', 'Count of missing values')

missing_stats

Task 3: ANALYTICS

Using Cluster method to identify which East African Countries have the highest death rate from illicit drug overdose  

-

SCALE NUMERIC COLUMNS

df_scaled<-df_2
df_scaled[,2:5] <- scale(df_2[,2:5])
df_scaled
# enter your code here

Using Elbow Method to find the ideal number of clusters

if(!require(devtools)) install.packages("devtools")
#devtools::install_github("kassambara/factoextra")

library(factoextra)
### Elbow method (look at the knee)

v1<-fviz_nbclust(df_scaled[,2:5], kmeans, method = "wss") +
geom_vline(xintercept = 3, linetype = 2)
girafe(ggobj = v1, width_svg = 13, height_svg = 7,
       options = list(opts_sizing(rescale = TRUE, width = 1.0)))

cumulative total

df_2<-df
df_2<-df_2%>%select(Entity,Deaths.Opioid,Deaths.Cocaine,Deaths.other.drug,Deaths.Amphetamine)%>%rename(Country='Entity')
df_2<-df_2%>%arrange(Country)%>%filter((Country=="Mauritius") | (Country=="Uganda") | (Country=="Rwanda")| (Country=="Kenya")| (Country=="Ethiopia")| (Country=="Tanzania")| (Country=="Eritrea")| (Country=="Madagascar")| (Country=="Somalia")| (Country=="Djibouti"))
#%>%select(Country,`Deaths.Opioid`, `Deaths.Cocaine`,`Deaths.other.drug`,`Deaths.Amphetamine`)

df_2 <- df_2 %>% 
  group_by(`Country`,) %>% 
  summarise(Deaths.Opioid=sum(`Deaths.Opioid`),Deaths.Cocaine = sum(`Deaths.Cocaine`),Deaths.other.drug = sum(`Deaths.other.drug`),Deaths.Amphetamine=sum(`Deaths.Amphetamine`))

df_scaled<-df_2
df_scaled[,2:5] <- scale(df_2[,2:5])

df_scaled

Clustering East african Countries

library(stats)

set.seed(123)
clusters <- kmeans(df_scaled[,2:5], 3, iter.max = 20, nstart = 25)


df_2$cluster <- as.factor(clusters$cluster)

df_2
# enter your code here

Ploting the clusters

v2 <- fviz_cluster(clusters, geom="point", data=df_2[,2:5],palette = "Set2") + 
  ggtitle("K=3") + 
  theme_minimal()
girafe(ggobj = v2, width_svg = 13, height_svg = 7,
       options = list(opts_sizing(rescale = TRUE, width = 1.0)))

mean_Cases - cumulative total

mean_data <-df_2 %>%
    group_by(cluster)%>%
    summarise(n = n(),
        Deaths.Opioid = mean(Deaths.Opioid),
        Deaths.Cocaine = mean(Deaths.Cocaine),
        Deaths.other.drug = mean(Deaths.other.drug),
        Deaths.Amphetamine = mean( Deaths.Amphetamine))
        

mean_data

Countries like Ethiopia and Tanzania Needs Attention From WHO.

-

the number one cause of drug overdose is found to be opioid

df_group3<-df_2%>%filter(cluster==1)
df_group3

Task 4: VIEWS

df_3<-df
df_3<-df_3%>%arrange(Year)%>%rename(Country='Entity')
df_3<-df_3%>%arrange(Country)%>% filter((Country=="Mauritius") | (Country=="Uganda") | (Country=="Rwanda")| (Country=="Kenya")| (Country=="Ethiopia")| (Country=="Tanzania")| (Country=="Eritrea")| (Country=="Madagascar")| (Country=="Somalia")| (Country=="Djibouti"))

df_3 <- df_3 %>% 
  group_by(`Country`,`Year`) %>% 
  summarise(Deaths.Opioid=sum(`Deaths.Opioid`),Deaths.Cocaine = sum(`Deaths.Cocaine`),Deaths.other.drug = sum(`Deaths.other.drug`),Deaths.Amphetamine=sum(`Deaths.Amphetamine`))




#%>%select(Country,`Deaths.Opioid`, `Deaths.Cocaine`,`Deaths.other.drug`,`Deaths.Amphetamine`)
df_33 <- df_3 %>% 
  group_by(`Country`) %>% 
  summarise(Deaths.Opioid=sum(`Deaths.Opioid`),Deaths.Cocaine = sum(`Deaths.Cocaine`),Deaths.other.drug = sum(`Deaths.other.drug`),Deaths.Amphetamine=sum(`Deaths.Amphetamine`))

df_5<-df_33
df_5$Deaths.Opioid<-format(round(df_5$Deaths.Opioid,0), nsmall=0)
df_5$Deaths.Cocaine<-format(round(df_5$Deaths.Cocaine,0), nsmall=0)
df_5$Deaths.other.drug<-format(round(df_5$Deaths.other.drug,0), nsmall=0)
df_5$Deaths.Amphetamine<-format(round(df_5$Deaths.Amphetamine,0), nsmall=0)

df_5

Creating a visual representation that conveys some insight to my analysis.

-

Total Deaths caused by Opioid in East Africa

library(treemapify)
library(ggiraph)

v2 <- ggplot(df_33, aes(area = Deaths.Opioid, fill = Deaths.Opioid,  label = Country)) +ggtitle("Deaths caused by Opioid") +
  geom_treemap() +
  geom_treemap_text(fontface = "italic", colour = "white", place = "centre",
                    grow = TRUE)

girafe(ggobj = v2, width_svg = 16, height_svg = 10,
  options = list(
    opts_sizing(rescale = TRUE, width = 0.8) )
)

Total Deaths caused by Cocaine in East Africa

theme_opts <- theme(
    plot.margin = margin(.25, 1, .25, .25, "cm"),
    plot.background = element_blank(),
    panel.background = element_blank(),
    legend.position = "none",
    axis.title.x=element_blank(),
    axis.title.y=element_blank(),
    axis.ticks.y=element_blank(),
    axis.ticks.x=element_blank(),
    axis.text.x=element_blank(),
    strip.placement = "outside",                      # Place facet labels outside x axis labels.
    strip.background = element_rect(fill = "white"),  # Make facet label background white.
    axis.title = element_blank()                     # Remove x and y axis titles.
)

# Make the plot
v1 <- ggplot(df_3, aes(x=Deaths.Cocaine, y=Country)) + 
  geom_bar(stat="identity", fill="#88b88a") +
   geom_text( data = df_33, size = 4, label = df_5$Deaths.Cocaine, hjust = 0, position = position_nudge(x = 12), color = 'gray30') +

  # scale_y_discrete(limits=c("Almond milk", "Oat milk", "Soy milk", "Rice milk","Cow's milk")) +
  #scale_fill_manual(values=c("Carbon Emissions (kg CO2eq)" = "#364e5d", "Land Use (m2)" = "#597e4e", "Water Use (L)" = "#587f89")) +
  #scale_color_manual(values=c("white" = "#ffffff", "black" = "#000000")) +
  coord_cartesian(clip="off") +
  labs(title = "total Death caused by cocaine in East Africa") +
  theme_opts

# Print the plot
v1

Total Deaths caused by Amphetamine in East Africa

theme_opts <- theme(
    plot.margin = margin(.25, 1, .25, .25, "cm"),
    plot.background = element_blank(),
    panel.background = element_blank(),
    legend.position = "none",
    axis.title.x=element_blank(),
    axis.title.y=element_blank(),
    axis.ticks.y=element_blank(),
    axis.ticks.x=element_blank(),
    axis.text.x=element_blank(),
    strip.placement = "outside",                      # Place facet labels outside x axis labels.
    strip.background = element_rect(fill = "white"),  # Make facet label background white.
    axis.title = element_blank()                     # Remove x and y axis titles.
)

# Make the plot
v1 <- ggplot(df_3, aes(x=Deaths.Amphetamine, y=Country)) + 
  geom_bar(stat="identity", fill="#0000FF") +
   geom_text( data = df_33, size = 4, label = df_5$Deaths.Amphetamine, hjust = 0, position = position_nudge(x = 12), color = 'blue') +

  # scale_y_discrete(limits=c("Almond milk", "Oat milk", "Soy milk", "Rice milk","Cow's milk")) +
  #scale_fill_manual(values=c("Carbon Emissions (kg CO2eq)" = "#364e5d", "Land Use (m2)" = "#597e4e", "Water Use (L)" = "#587f89")) +
  #scale_color_manual(values=c("white" = "#ffffff", "black" = "#000000")) +
  coord_cartesian(clip="off") +
  labs(title = "total Death caused by Amphetaminecocaine in East Africa") +
  theme_opts

# Print the plot
v1

Total Deaths caused by other Drugs in East Africa

theme_opts <- theme(
    plot.margin = margin(.25, 1, .25, .25, "cm"),
    plot.background = element_blank(),
    panel.background = element_blank(),
    legend.position = "none",
    axis.title.x=element_blank(),
    axis.title.y=element_blank(),
    axis.ticks.y=element_blank(),
    axis.ticks.x=element_blank(),
    axis.text.x=element_blank(),
    strip.placement = "outside",                      # Place facet labels outside x axis labels.
    strip.background = element_rect(fill = "white"),  # Make facet label background white.
    axis.title = element_blank()                     # Remove x and y axis titles.
)

# Make the plot
v1 <- ggplot(df_3, aes(x=Deaths.other.drug, y=Country)) + 
  geom_bar(stat="identity", fill="#FFFF00") +
   geom_text( data = df_33, size = 4, label = df_5$Deaths.other.drug, hjust = 0, position = position_nudge(x = 12), color = 'yellow') +

  # scale_y_discrete(limits=c("Almond milk", "Oat milk", "Soy milk", "Rice milk","Cow's milk")) +
  #scale_fill_manual(values=c("Carbon Emissions (kg CO2eq)" = "#364e5d", "Land Use (m2)" = "#597e4e", "Water Use (L)" = "#587f89")) +
  #scale_color_manual(values=c("white" = "#ffffff", "black" = "#000000")) +
  coord_cartesian(clip="off") +
  labs(title = "total Death caused by other Drugs in East Africa") +
  theme_opts

# Print the plot
v1

Task 5: STORYTELLING

-

Drug overdoes effect on East Africa

Objective: identifying a country that has the highest drug overdose death rate in East african.

First let us identify and list illicit drugs that are responsible for the death of many in East Africa.

  • opioid use disorder(OUD)
  • Cocaine
  • Amphetamine
  • other.drugs
knitr::include_graphics("assets/drug-OD1.Jpg", dpi=00)

masthead <- "assets/drug-OD1.Jpg"

Drugs take you to hell, disguised as heaven.
— Donald Lyn

Out of all the illicit drugs, opioid use disorder(OUD) is the number one cause of death in east Africa, for this matter opioid deserves our attention.

mean_Cases - cumulative total

mean_data <-df_2 %>%
    group_by(cluster)%>%
    summarise(n = n(),
        Deaths.Opioid = mean(Deaths.Opioid),
        Deaths.Cocaine = mean(Deaths.Cocaine),
        Deaths.other.drug = mean(Deaths.other.drug),
        Deaths.Amphetamine = mean( Deaths.Amphetamine))
        

mean_data

How do people misuse prescription opioids?

Prescription opioids used for pain relief are generally safe when taken for a short time and as prescribed by a doctor, but they can be misused. People misuse prescription opioids by:

  • taking the medicine in a way or dose other than prescribed
  • taking someone else’s prescription medicine
  • taking the medicine for the effect it causes-to get high

When misusing a prescription opioid, a person can swallow the medicine in its normal form. Sometimes people crush pills or open capsules, dissolve the powder in water, and inject the liquid into a vein. Some also snort the powder, pluse the misuse of Opioid has given birth to Heroin.

Heroin is an opioid drug made from morphine, a natural substance taken from the seed pod of the various opium poppy plants grown in Southeast.
— National Institute on Drug Abuse

Opioid use disorde(OUD) occurs in sub-Saharan Africa, with attendant clinical and social costs. Evidence-based policies and health system resources are needed to promote OUD prevention and management From WHO and other health organization.

Number of Deaths Opiod by percent

Though the Government of Ethiopia has adopted a national drug control master plan, it has a lot to do to prevent the growth of death rate,

df_deaths <- df_33 %>% select(Country, Deaths.Opioid)

#remove effects where deaths is NA
df_deaths <- df_deaths[!is.na(df_deaths$Deaths.Opioid), ]

# creating a percentage column
df_deaths <- mutate(df_deaths, Percent=round(100*df_deaths$Deaths.Opioid/sum(df_deaths$Deaths.Opioid)))
df_deaths <- df_deaths %>% filter(df_deaths$Percent > 2)

#df_deaths
df_deathsx<-df_deaths
df_deathsx$Deaths.Opioid<-format(round(df_deathsx$Deaths.Opioid), nsmall=0)
df_deathsx

Number of Deaths in East africa per Opioid death

Ethiopia scorded the highst death rate in East africa.

theme_opts <- theme_minimal() + theme(
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.minor.y = element_blank(),
    axis.ticks = element_line(),
    panel.background = element_rect(fill = "white", colour = "white"), 
    legend.position = "right"
)

area <- ggplot(df_deaths, aes(x="", y= Deaths.Opioid, fill= Country)) +
        geom_col(color="black")+
        geom_text_repel(aes(label = paste(df_deathsx$Deaths.Opioid,"-",df_deaths$Percent,"%")),
             color = "white",       
             position = position_stack(vjust = 0.5),
            show.legend = FALSE) +
       coord_polar(theta = "y")+
        labs(title = "Number of Deaths per Opioid death", subtitle = "Preliminary Analysis") +
        theme_minimal() +
        theme_opts

area

ggsave(filename = "assets/ethiopia-disaster-deaths.svg", plot = area, width=13, height=4)

Number of death over the Years in Ethiopia

df_7<-df
df_7<-df_7%>%arrange(Year)%>%rename(Country='Entity')
df_7<-df_7%>%arrange(Country)%>% filter( (Country=="Ethiopia"))

df_7$Deaths.Opioid<-format(round(df_7$Deaths.Opioid), nsmall=0)


#Wrangling data
df_7 <- df_7 %>% select(Year,Deaths.Opioid)

df_8<-df_7
df_8 <- transform(df_7,Deaths.Opioid = as.numeric(Deaths.Opioid))
df_8
#fit simple linear regression model
#model <- lm(df_temp$Deaths~df_temp$Year)

#view model summary
#summary(model)

#Call:
#lm(formula = df_temp$Deaths~df_temp$Year)

#From the model we see, Deaths=(-6325.840) + 3.303*(f_temp$Year)
theme_opts <- theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black", size = 8),
    plot.caption = element_text(color = "#555555", size = 8),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    # axis.text.x = element_text(vjust = 12),
    panel.border = element_blank(),
    panel.background = element_blank(),
    panel.grid.minor = element_blank(), # remove minor gridlines
    panel.grid.major.x = element_blank(), # remove x (vertical) gridlines
    legend.title = element_blank(), # remove legend title
    legend.text = element_text(color = "black", size = 8),
    legend.position='top'
  )


# Plot
v1 <- ggplot(df_8, aes(x=Year, y=Deaths.Opioid)) + 
          geom_line(aes(y=Deaths.Opioid), color="blue") +
         
  geom_area(color = "#458cb6", fill= "#a1b5d2", alpha=1.0 , size=0.5) +
   #geom_hline(yintercept = c(seq(0, 100, 10)), color="#ffffff", size=0.25, alpha=0.2) + # gridlines in the forefront
 scale_x_continuous(breaks=c(1990,1993,1996,1999,2002,2005,2007,2010,2013,2016,2019)) +
  scale_y_continuous(breaks=seq(0, 300, 10)) +
  labs( title = "opioid data for Ethiopia!",
        subtitle = "total death of opioid from 1990 to 2017") +
  theme_bw() +
  
           annotate("text", x = 1997, y = 110, 
             label = paste0("In 1997 - the highest peak", "\n", "Deaths Number = 110"), 
             hjust = "left", vjust = 0, color = "#000000", size = 4, fontface = 1) +
  
           annotate("text", x = 2016, y = 97, 
             label = paste0(" 2017 peak", "\n", "Deaths=97"), 
             hjust = "left", vjust = 0, color = "#000000", size = 4, fontface = 1) +
  theme_opts

v1

ANALYTICS PART 2

using Time serious to see ethiopian Opioid death rate in Time

df_22<-df
df_22<-df_22%>%select(Entity,Year,Deaths.Opioid)%>%rename(Country='Entity')
 
df_22<-df_22%>%arrange(Country)%>%filter( (Country=="Ethiopia"))
#%>%select(Country,`Deaths.Opioid`, `Deaths.Cocaine`,`Deaths.other.drug`,`Deaths.Amphetamine`)

df_22 <- df_22 %>% 
  group_by(`Year`,) %>% 
  summarise(Deaths.Opioid=sum(`Deaths.Opioid`))

mean_data1 <-df_22%>%
  rename(Deaths.Opioid.mean='Deaths.Opioid') %>%
    group_by(Year)%>%
    summarise(
        Deaths.Opioid.mean = mean(Deaths.Opioid.mean))
        

mean_data1

Changing “year” to a date, and saving date as a new column called “date_char

df_23 <-mean_data1 %>% 
select(date=Year,Deaths.Opioid.mean)%>%
arrange(date)
df_23$date <- as.Date(mean_data1$Year,"1901-01-01")

 
df_23

Calculate Moving Average

library(TTR)
df_24 <- df_23%>%mutate(MA=ALMA(Deaths.Opioid.mean, n = 9, offset = 0.85, sigma = 6))

df_24

Visualizing the two variables onto one graph

ggplot(data=df_24, aes(x=date)) +
geom_line(mapping=aes(y=MA, color="MA"),group = 1,size=1)+
geom_line(mapping=aes(y=Deaths.Opioid.mean, color="mean_Deaths.Opioid"),group = 1,size=1)+
  theme(panel.grid = element_blank(), 
        axis.ticks = element_blank(), 
        strip.background = element_blank(),
        panel.background = element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank()) +
  scale_color_manual(values = c(Deaths.Opioid.mean="black",
                                MA="red"))

# References ## The citations and data sources used for this case

Task 6: PACKAGE

  • Knitr your project to an html page.
  • Provide the source data, the .Rmd and .html documents as a compressed package.